Taxas finitas de decomposição: uma nova abordagem na estimativa dos parâmetros de Arrhenius valendo-se dos algoritmos de partículas SIR e ASIR

• Ano de defesa: 2016
• Autor(a) principal: Klippel, Míriam Suély
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Espírito Santo; BR; Mestrado em Engenharia Mecânica; Centro Tecnológico; UFES; Programa de Pós-Graduação em Engenharia Mecânica
• Palavras-chave em Português: Particle filters SIR, ASIR; Kinetics chemica; Finite rate of decomposition; Arrhenius model; Filtro partículas SIR e ASIR; Taxa finita de decomposição; Modelo de Arrhenius; Cinética química; Filtros e filtração - Partículas; Engenharia Mecânica; 621
• Link de acesso: http://repositorio.ufes.br/handle/10/9814

The thermochemical conversion plays an important role in power generation. The fuels have a variety of thermochemical properties derived from their source which requires a complexity in determining patterns in the thermal structure and chemical conversion processes. Understanding the thermochemical and the consequent chemical kinetics involved in the chemical species formation during the solid fuel conversion process is an important factor for numerical modeling of pyrolysis and combustion processes. For these reasons, the present work is aimed to deal with the application of particle filters in the Arrhenius parameters estimation. Sampling Importance Resampling (SIR) and Auxiliary Sampling Importance Resampling (ASIR) were then used. The methodology consists on to make a wide discussion about the various methods of Arrhenius parameters estimation. Then, the particle filter algorithms are adapted to estimate the kinetic parameters, which are subsequently implemented in a computer code that receives ThermoGravimetric data and the reaction model system. A first test was carried out in a single reaction step for pyrolysis of cellulose. The results were satisfactory. Finally, a new approach to estimate kinetic parameters was proposed and called Finite Rate of Decomposition. The best results for this approach were using the likelihood based on TG data that fitted very well the weight loss curve. The inaccuracies of the new method are attenuated with a gradual increase in the particles number or by decrease the integration time for each time step.